Cortical thickness correlates of pain and temperature sensitivity
Bibliographic record
Abstract
It is well established that there is individual variability in pain and temperature sensitivity. Functional brain imaging studies have found that interindividual heat pain variability correlates with brain activity in sensory and pain modulation areas. Thus, it is possible that these individual differences are associated with variability in gray matter thickness of cortical regions involved in thermoreception and pain. To test this, we investigated the relationship between thermal thresholds and cortical thickness in 80 healthy subjects. Subjects underwent a psychophysical session to determine their cool detection (CD), warm detection (WD), cold pain (CP), and heat pain (HP) threshold. A high-resolution structural magnetic resonance imaging scan was acquired for each subject. We correlated each threshold measure to cortical thickness of regions associated with thermoreception and pain. The mean (± SD) thresholds were 30.7 °C (± 0.8) for CD, 33.8 °C (± 0.7) for WD, 11.7 °C (± 9.7) for CP, and 45.3 °C (± 2.8) for HP. The brain gray matter analysis revealed a strong correlation between greater thermal and pain sensitivity and cortical thickening of the primary somatosensory cortex. Additionally, greater sensitivity to cool stimuli correlated with cortical thickening in the paracentral lobule, and greater WD correlated with cortical thinning in the anterior midcingulate cortex. We also found that greater HP sensitivity correlated with thickening in the posterior midcingulate cortex and the orbitofrontal cortex. These cortical gray matter correlates of thermal and pain sensitivity provide a neural basis for individual differences in thermal sensitivity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".